Effectiveness of Antidepressants in Combination with Psychotherapy.

BACKGROUND: Consensus-guidelines for prescribing antidepressants recommend that clinicians should be vigilant to match antidepressants to patient's medical history but provide no specific advice on which antidepressant is best for a given medical history. AIMS OF THE STUDY: For patients with major depression who are in psychotherapy, this study provides an empirically derived guideline for prescribing antidepressant medications that fit patients' medical history. METHODS: This retrospective, observational, cohort study analyzed a large insurance database of 3,678,082 patients. Data was obtained from healthcare providers in the U.S. between January 1, 2001, and December 31, 2018. These patients had 10,221,145 episodes of antidepressant treatments. This study reports the remission rates for the 14 most commonly prescribed single antidepressants (amitriptyline, bupropion, citalopram, desvenlafaxine, doxepin, duloxetine, escitalopram, fluoxetine, mirtazapine, nortriptyline, paroxetine, sertraline, trazodone, and venlafaxine) and a category named "Other" (other antidepressants/combination of antidepressants). The study used robust LASSO regressions to identify factors that affected remission rate and clinicians' selection of antidepressants. The selection bias in observational data was removed through stratification. We organized the data into 16,770 subgroups, of at least 100 cases, using the combination of the largest factors that affected remission and selection bias. This paper reports on 2,467 subgroups of patients who had received psychotherapy. RESULTS: We found large, and statistically significant, differences in remission rates within subgroups of patients. Remission rates for sertraline ranged from 4.5% to 77.86%, for fluoxetine from 2.86% to 77.78%, for venlafaxine from 5.07% to 76.44%, for bupropion from 0.5% to 64.63%, for desvenlafaxine from 1.59% to 75%, for duloxetine from 3.77% to 75%, for paroxetine from 6.48% to 68.79%, for escitalopram from 1.85% to 65%, and for citalopram from 4.67% to 76.23%. Clearly these medications are ideal for patients in some subgroups but not others. If patients are matched to the subgroups, clinicians can prescribe the medication that works best in the subgroup. Some medications (amitriptyline, doxepin, nortriptyline, and trazodone) always had remission rates below 11% and therefore were not suitable as single antidepressant therapy for any of the subgroups. DISCUSSIONS: This study provides an opportunity for clinicians to identify an optimal antidepressant for their patients, before they engage in repeated trials of antidepressants. IMPLICATIONS FOR HEALTH CARE PROVISION AND USE: To facilitate the matching of patients to the most effective antidepressants, this study provides access to a free, non-commercial, decision aid at http://MeAgainMeds.com. IMPLICATIONS FOR HEALTH POLICIES:  Policymakers should evaluate how study findings can be made available through fragmented electronic health records at point-of-care. Alternatively, policymakers can put in place an AI system that recommends antidepressants to patients online, at home, and encourages them to bring the recommendation to their clinicians at their next visit. IMPLICATIONS FOR FURTHER RESEARCH:  Future research could investigate (i) the effectiveness of our recommendations in changing clinical practice, (ii) increasing remission of depression symptoms, and (iii) reducing cost of care. These studies need to be prospective but pragmatic. It is unlikely random clinical trials can address the large number of factors that affect remission.

Neuroanatomical markers of psychotic experiences in adolescents: A machine-learning approach in a longitudinal population-based sample.

It is important to identify accurate markers of psychiatric illness to aid early prediction of disease course. Subclinical psychotic experiences (PEs) are important risk factors for later mental ill-health and suicidal behaviour. This study used machine learning to investigate neuroanatomical markers of PEs in early and later stages of adolescence. Machine learning using logistic regression using Elastic Net regularization was applied to T1-weighted and diffusion MRI data to classify adolescents with subclinical psychotic experiences vs. controls across 3 timepoints (Time 1:11-13 years, n = 77; Time 2:14-16 years, n = 56; Time 3:18-20 years, n = 40). Neuroimaging data classified adolescents aged 11-13 years with current PEs vs. controls returning an AROC of 0.62, significantly better than a null model, p = 1.73e-29. Neuroimaging data also classified those with PEs at 18-20 years (AROC = 0.59;P = 7.19e-10) but performance was at chance level at 14-16 years (AROC = 0.50). Left hemisphere frontal regions were top discriminant classifiers for 11-13 years-old adolescents with PEs, particularly pars opercularis. Those with future PEs at 18-20 years-old were best distinguished from controls based on left frontal regions, right-hemisphere medial lemniscus, cingulum bundle, precuneus and genu of the corpus callosum (CC). Deviations from normal adolescent brain development in young people with PEs included an acceleration in the typical pattern of reduction in left frontal thickness and right parietal curvature, and accelerated progression of microstructural changes in right white matter and corpus callosum. These results emphasise the importance of multi-modal analysis for understanding adolescent PEs and provide important new insights into early phenotypes for psychotic experiences.

Psychotic experiences in childhood are associated with increased structural integrity of the left arcuate fasciculus - A population-based case-control study.

Around 1 in 5 children under 13 years old experience sub-clinical psychotic experiences (PEs) like hallucinations and delusions. While PEs in childhood are a significant risk factor for adult psychotic disorders, the majority of those experiencing childhood PEs do not develop a psychotic disorder. Individual differences in regional brain maturation rates may be responsible for this age-related and often transient emergence of PEs. Fronto-temporal association tracts undergo extensive maturation and myelination throughout childhood and adolescence, thus we focus on individual differences in one such tract, the arcuate fasciculus. A normative population-based sample of children (aged 11-13) attended a clinical interview and MRI (n = 100), 25 of whom were identified as reporting strong PEs. This group had reduced mean and radial diffusivity in the arcuate fasciculus compared with a group of matched controls (n = 25) who reported no PEs. The group difference was greater in the left hemisphere than the right. Mediation analyses showed that this group difference was driven predominantly by perceptual disturbances and an along-tract analysis showed that the group difference was greatest approximately halfway between the frontal and temporal termination points of the tract (adjacent to the left lateral ventricle). This study is the first to investigate links between arcuate fasciculus diffusivity and psychotic experiences in a population sample of children.